Detecting Video Game Player Burnout With the Use of Sensor Data and Machine Learning

Smerdov, Anton; Somov, Andrey; Burnaev, Evgeny; Zhou, Bo; Lukowicz, Paul

doi:10.1109/jiot.2021.3074740

Cited by 19 publications

(18 citation statements)

References 42 publications

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“…We have found that 20 s time step and 3–5 min behavior forecasting horizon are the most natural parameters for CS:GO, but potential users can set up any other hyperparameters depending on their scenario. In the study [ 58 ] focused on League of Legends, authors find time step 1 s and forecasting horizon 10 s close to optimal for the game, although forecasting horizons up to 90 s are still reasonable. The difference in optimal forecasting horizons can be explained by higher frequency of kill/death/assist events in League of Legends, thus less time is needed to estimate player performance.…”

Section: Resultsmentioning

confidence: 99%

“…In [ 58 ], the authors investigate how to predict whether the player wins the next encounter in League of Legends (LoL) using the sensor data. Their best model (Transformer network) achieves ROC AUC score (the area under the ROC curve (AUC), where ROC stands for the receiver operating characteristic) of 0.706 in predicting whether the player will win the encounter occurring 10 seconds later, and they built the system predicting the ‘player burnout’ with 73.5% precision and 88.3% recall.…”

Section: Related Workmentioning

confidence: 99%

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AI-enabled prediction of video game player performance using the data from heterogeneous sensors

Smerdov

Somov

Burnaev

et al. 2022

Multimed Tools Appl

Self Cite

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The emerging progress of video gaming and eSports lacks the tools for ensuring high-quality analytics and training in professional and amateur eSports teams. We report on an Artificial Intelligence (AI) enabled solution for predicting the eSports player in-game performance using exclusively the data from sensors. For this reason, we collected the physiological, environmental, and the smart chair data from professional and amateur players. The player performance is assessed from the game logs in a multiplayer game for each moment of time using a recurrent neural network. We have investigated an attention mechanism improves the generalization of the network and provides a straightforward feature importance as well. The best model achieves Area Under the Receiver Operating Characteristic Curve (ROC AUC) score 0.73 in predicting whether a player will perform better or worse in the next 240 seconds based on in-game metrics. The prediction of the performance of a particular player is realized although their data are not utilized in the training set. The proposed solution has a number of promising applications for professional eSports teams and amateur players, such as a learning tool or performance monitoring system.

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Section: Resultsmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

AI-enabled prediction of video game player performance using the data from heterogeneous sensors

Smerdov

Somov

Burnaev

et al. 2022

Multimed Tools Appl

Self Cite

View full text Add to dashboard Cite

show abstract

“…Considering other aspects that machine learning could potentially contribute in predicting the win rate, there are research studying how machine learning methods can be used to measure the level of skills for each player in an FPS game [17][18]. Also, some research pay attention to predict the tiredness and behavioral features that could potentially be influential factors of winning a match as well [19][20]. For example, paper [19] uses Logistic Regression, K-Nearest Neighbors, and GRU Neural Network to measure outside-game features like chair movements, pulse, muscle activity, electroencephalogram, and environmental temperature.…”

Section: Machine Learning Applications In Predicting the Outcome Of F...mentioning

confidence: 99%

“…Also, some research pay attention to predict the tiredness and behavioral features that could potentially be influential factors of winning a match as well [19][20]. For example, paper [19] uses Logistic Regression, K-Nearest Neighbors, and GRU Neural Network to measure outside-game features like chair movements, pulse, muscle activity, electroencephalogram, and environmental temperature. It can successfully predict whether a player will win or lose the next encounter fight with accuracy at around 70-73.5 percent.…”

Section: Machine Learning Applications In Predicting the Outcome Of F...mentioning

confidence: 99%

The Application of Machine Learning in Predicting the Results of Popular eSports Games: Win Rate Prediction in MOBA and FPS Games

Peng

2024

HSET

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Electronic Sports (eSports) are one of the most prevalent entertainments in modern society. As eSports is gaining more and more popularity over time, a proper evaluation system that can accurately analyze and predict players’ performance is indispensable. Therefore, machine learning is proposed as an effective prediction tool in the studying of data from eSports matches so that eSports teams can utilize it to ameliorate their future accomplishments. Two popular eSports game types (Multiplayer Online Battle Arena and First Person Shooter) are analyzed. Common features they share include communication, equipment, economy, and so on. Correct hero pick is the most predominant factor in MOBA games. Research shows that machine learning models like Logistic Regression, Support Vector Machine, and Neural Networks can effectively combine hero selection data to predict the outcome of a game. On the other hand, the most important factor in FPS games is the in-game equipment each round. Additional research proves that Logistic Regression, K-Nearest Neighbors, Random Forest, and Neural Networks are the most powerful models for predicting the team win rate. This paper can serve as an instruction for applying machine learning methods to making predictions in eSports games. Meanwhile, it can provide available techniques to help professional eSports teams improve their competitive level and future in-game understanding as well.

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“…However, stress is not the only interesting indicator to consider in Esports environments, whether competitive or not, peripheral physiology can also provide insight into various aspects of cognitive/emotional information processing, such as polarity, emotion, engagement, boredom, frustration, etc. The benefits of extracting this information are evident, as exemplified by Smerdov et al 2020. They performed a very comprehensive sensory analysis that results in the submission of a dataset collected from professional and amateur teams in 22 League of Legends video matches, including the simultaneous recording of the physiological (i.e.…”

Section: Related Workmentioning

confidence: 99%

Physiology on Esports, a League of Legends study

Berga¹,

Pereda²,

Filippi³

et al. 2023

Preprint

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Esports gaming is an area in which videogame players need to cooperate and compete with each other, influencing their cognitive load, processing, stress, and as well as social skills. Here it is unknown to which extent traditional videogame play (with a desktop setting) can affect the physiological responses of players' autonomic nervous system. For such, we propose a study where we have measured distinct electrodermal and cardiac activity metrics over competitive players during several League of Legends gameplay sessions. We mainly found that game performance (whether winning or losing the game) significantly affects both EDA and ECG, where players who lost the game showed higher stress-related physiological responses, as compared to winning players. We also found that important specific in-game events such as "Killing", "Dying" or "Destroying Turret" significantly increased both electrodermal and cardiac activity over players more than other less-relevant events such as "Placing Wards" or "Destroying Turret Plates". Finally, by analyzing activity on player roles we found different trends of activity on these measurements, that may foster the exploration of esports gaming effects on human physiology in future studies.

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Detecting Video Game Player Burnout With the Use of Sensor Data and Machine Learning

Cited by 19 publications

References 42 publications

AI-enabled prediction of video game player performance using the data from heterogeneous sensors

AI-enabled prediction of video game player performance using the data from heterogeneous sensors

The Application of Machine Learning in Predicting the Results of Popular eSports Games: Win Rate Prediction in MOBA and FPS Games

Physiology on Esports, a League of Legends study

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